Information processing method and terminal device

ABSTRACT

Disclosed are an information processing method and a terminal device. The method comprises: acquiring first information, wherein the first information is information to be processed by a terminal device; calling an operation instruction in a calculation apparatus to calculate the first information so as to obtain second information; and outputting the second information. By means of the examples in the present disclosure, a calculation apparatus of a terminal device can be used to call an operation instruction to process first information, so as to output second information of a target desired by a user, thereby improving the information processing efficiency. The present technical solution has advantages of a fast computation speed and high efficiency.

TECHNICAL FIELD

The present disclosure relates to the technical field of information technology, and particularly to an information processing method and related products.

BACKGROUND

With the growing information technology and people's ever-increasing demand, the need for timeliness of information becomes stronger. At present, terminal devices obtain information by general-purpose processors. For instance, a general-purpose processor may run a specified application to implement object detection and classification, or to implement identification of the current environment, etc.

However, in practical applications, this way of obtaining information by a general-purpose processor running a software program may be limited by the operating speed of the general-purpose processor, and in particular, when the general-purpose processor has a large load, the efficiency of obtaining information may be low and the delay may be long.

SUMMARY

Examples of the present disclosure provide an information computation method and related products, which can increase processing speed and efficiency of a computation device.

In a first aspect, an example of the present disclosure provides an information processing method which is applied to a computation device, where the computation device includes a communication unit and an operation unit. The method includes:

controlling, by the computation device, the communication unit to obtain a target image to be processed, where the target image includes feature identification objects which are used to identify the target image or identify objects included in the target image; and

controlling, by the computation device, the operation unit to obtain an operation instruction and call the operation instruction to classify feature identification objects in the target image, so as to obtain a target classification result, where

the target classification result is used to indicate target classifications to which the feature identification objects belong, and the operation instruction is a pre-stored instruction for object classification.

In some possible examples, the computation device further includes a register unit and a controller unit. The controlling, by the computation device, the operation unit to obtain an operation instruction and call the operation instruction to classify feature identification objects in the target image, so as to obtain a target classification result includes:

controlling, by the computation device, the controller unit to fetch a first operation instruction and a second operation instruction from the register unit, and sending, by the computation device, the first operation instruction and the second operation instruction to the operation unit;

controlling, by the computation device, the controller unit to call the first operation instruction to perform feature extraction on the target image to obtain a feature image; and

controlling, by the computation device, the controller unit to call the second operation instruction to perform object classification on the feature image to obtain a target classification result, where

the first operation instruction is a preset instruction for feature extraction, and the second operation instruction is a preset instruction for object classification.

In some possible examples, the controlling, by the computation device, the controller unit to call the second operation instruction to perform object classification on the feature image to obtain a target classification result includes:

controlling, by the computation device, the operation unit to call the second computation instruction to perform object classification on the feature image to obtain a plurality of classification results, where the classification results include classification scores; and

controlling, by the computation device, the operation unit to select a classification result corresponding to a classification score exceeding a preset threshold from the plurality of classification results as the target classification result.

In some possible examples, the controlling, by the computation device, the operation unit to call the first operation instruction to perform feature extraction on the target image to obtain a feature image includes:

controlling, by the computation device, the operation unit to perform feature extraction on the target image based on an operation instruction set of at least one thread to obtain the feature image, where the operation instruction set includes at least one first operation instruction, and an order of calling the first operation instruction in the operation instruction set is customized by a user side or a terminal side.

In some possible examples, the controlling, by the computation device, the communication unit to obtain a target image to be processed includes:

controlling, by the computation device, the communication unit to obtain an original image to be processed input by a user; and

controlling, by the computation device, the operation unit to pre-process the original image to obtain a target image to be processed, where the pre-processing includes one or more of the following processing manners: translation, scaling transformation, non-linear transformation, normalization, format conversion, data deduplication, processing of data exception, and data missing filling.

In some possible examples, the computation device further includes a data access unit and a storage medium, where

the computation device controls the operation unit to send the target classification result to the data access unit for storing the same in the storage medium.

In some possible examples, the operation unit includes a primary operation module and a plurality of secondary operation modules, where the primary operation module is interconnected with the plurality of secondary operation modules by an interconnection module, and when the operation instruction is a convolution operation instruction,

the calling the operation instruction to classify the feature identification objects in the target image includes:

controlling, by the computation device, the secondary operation modules to implement a convolution operation of input data and convolution kernels in a convolution neural network algorithm, where the input data is the target image and the convolution neural network algorithm corresponds to the convolution operation instruction;

controlling, by the computation device, the interconnection module to implement data transfer between the primary operation module and the secondary operation modules; before a forward operation of a neural network fully connected layer starts, transferring, by the primary operation module, the input data to each secondary operating through the interconnection module; and after the computation of the secondary operation modules is completed, splicing, by the interconnection module, output scalars of the respective secondary operation modules stage by stage to obtain an intermediate vector, and sending the intermediate vector back to the primary operation module; and

splicing, by the primary operation module, intermediate vectors corresponding to all input data into an intermediate result, and performing subsequent operations on the intermediate result.

In some possible examples, the performing subsequent operations on the intermediate result includes:

controlling, by the computation device, the primary operation module to add bias data to the intermediate result, and then performing an activation operation.

In some possible examples, the primary operation module includes a first operation unit, where the first operation unit includes a vector addition unit and an activation unit,

the steps of controlling, by the computation device, the primary operation module to add bias data to the intermediate result, and then performing an activation operation include:

controlling, by the computation device, the vector addition unit to implement a bias operation of a convolutional neural network operation and perform element-wise addition on bias data and the intermediate result to obtain a bias result; and

controlling, by the computation device, the activation unit to perform an activation function operation on the bias result.

In some possible examples, the primary operation module includes a first storage unit, a first operation unit, a first data dependency determination unit, and a first storage unit;

the computation device controls the first storage unit to cache input data and output data used by the primary operation module during a computation process, where the output data includes the object classification result;

the computation device controls the first operation unit to perform various operational functions of the primary operation module;

the computation device controls the data dependency determination unit to ensure that there is no consistency conflict in reading data from and writing data to the first storage unit, read an input neuron vector from the first storage unit, and send the vector to the secondary operation modules through the interconnection module; and

an intermediate result vector from the interconnection module is sent to the first operation unit.

In some possible examples, each secondary operation modules include a second operation unit, where the second operation unit includes a vector multiplication unit and an accumulation unit,

the controlling, by the computation device, the secondary operation modules to perform a convolution operation of input data and a convolution kernel in a convolution neural network algorithm includes:

controlling, by the computation device, the vector multiplication unit to perform a vector multiplication operation of the convolution operation; and

controlling, by the computation device, the accumulation unit to perform an accumulation operation of the convolution operation.

In some possible examples, each secondary operation module includes a second operation unit, a second data dependency determination unit, a second storage unit, and a third storage unit,

the computation device controls the second operation unit to perform various arithmetic and logical operations of the secondary operation modules,

controlling, by the computation device, the second data dependency determination unit to perform a reading/writing operation on the second storage unit and the third storage unit during a computation process and ensure that there is no consistency conflict between the reading and writing operations on the second storage unit and the third storage unit;

controlling, by the computation device, the second storage unit to cache input data and an output scalar and an output scalar obtained from the computation performed by the secondary operation module; and

controlling, by the computation device, the third storage unit to cache a convolution kernel required by the secondary operation module during a computation process.

In some possible examples, the first data dependency or the second data dependency ensures that there is no consistency conflict in reading and writing in the following manners: storage addresses corresponding to data/instructions stored in the corresponding storage unit do not overlap; or determining whether there is dependency between a control signal that has not been executed and data of a control signal that is being executed, if there is no dependency, the control signal is allowed to be issued immediately, otherwise, the control signal is not allowed to be issued until all control signals on which the control signal is dependent have been executed, where

the computation device controls the controller unit to obtain an operation instruction from the register unit and decode the operation instruction into the control signal for controlling behavior of other modules, where the other modules include the main the primary operation module and the plurality of secondary operation modules.

In some possible examples, the computation device controls the plurality of secondary operation modules to compute respective output scalars in parallel by using the same input data and respective convolution kernels.

In some possible examples, an activation function active used by the primary operation module may be any of the following non-linear functions: sigmoid, tanh, relu, softmax, or may be a linear function.

In some possible examples, the interconnection module forms a data channel for continuous or discrete data between the primary operation module and the plurality of secondary operation modules. The interconnection module has any of the following structures: a tree structure, a ring structure, a grid structure, a hierarchical interconnection, and a bus structure.

In a second aspect, an example of the present disclosure provides a computation device which includes a function unit configured to perform the methods of the first aspect.

In a third aspect, an example of the present disclosure provides a computer readable storage medium on which a computer program used for electronic data exchange is stored, where the computer program enables a computer to perform the methods of the first aspect.

In a fourth aspect, an example of the present disclosure further provides a computer program product which includes a non-transitory computer readable storage medium storing a computer program. The computer program may cause a computer to perform the methods of the first aspect.

In a fifth aspect, an example of the present disclosure provides a chip which includes the computation device of the second aspect.

In a sixth aspect, an example of the present disclosure provides a chip package structure which includes the chip of the fifth aspect.

In a seventh aspect, an example of the present disclosure provides a board card which includes the chip package structure of the sixth aspect.

In an eighth aspect, an example of the present disclosure provides an electronic device which includes the board card of the seventh aspect.

In some examples, the electronic device includes a data processing device, a robot, a computer, a printer, a scanner, a tablet, a smart terminal, a mobile phone, a traffic recorder, a navigator, a sensor, a webcam, a server, a cloud-based server, a camera, a video camera, a projector, a watch, a headphone, a mobile storage, a wearable device, a vehicle, a household appliance, and/or a medical equipment.

In some examples, the vehicle includes an airplane, a ship, and/or a car. The household electrical appliance includes a television, an air conditioner, a microwave oven, a refrigerator, a rice cooker, a humidifier, a washing machine, an electric lamp, a gas cooker, and a range hood. The medical equipment includes a nuclear magnetic resonance spectrometer, a B-ultrasonic scanner, and/or an electrocardiograph.

Technical effects of implementing the examples of the present disclosure are as follows:

it can be seen that through the examples of the present disclosure, a computation device may control a communication unit to obtain a target image to be processed, where the target image includes feature identification objects which are used to identify the target image or identify objects included in the target image; and the computation device may control an operation unit to call an operation instruction to classify the feature identification objects in the target image, so as to obtain a target classification result, where the target classification result is used to indicate target classifications to which the feature identification objects belong, and the operation instruction is a pre-stored instruction for object classification; in this way, a target image or feature recognition objects in the target image may be accurately, quickly, and comprehensively identified and classified; compared with the prior art which uses general-purpose processors to implement identification and classification, the examples of the present disclosure has beneficial effects of lower power consumption and faster speed.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to illustrate the technical solutions in the examples of the present disclosure more clearly, the drawings to be used in the description of the examples are briefly explained below. Obviously, the drawings in the description below are some examples of the present disclosure. Other drawings can be obtained according to the disclosed drawings without any creative effort by those skilled in the art.

FIG. 1A is a structural diagram of a computation device according to an example of the present disclosure.

FIG. 1B is a schematic flowchart of a convolutional neural network algorithm.

FIG. 1C is a schematic diagram of an instruction of a device supporting a convolutional neural network forward operation according to an example of the present disclosure.

FIG. 1D is a block diagram of an overall structure of a device for performing a convolution neural network forward operation according to an example of the present disclosure.

FIG. 1E is a structural diagram of an H-tree module (an implementation of an interconnection module) of a device for performing a convolution neural network forward operation according to an example of the present disclosure.

FIG. 1F is a block diagram of a structure of a primary operation module of a device for performing a convolution neural network forward operation according to an example of the present disclosure.

FIG. 1G is a block diagram of a structure of a secondary operation module of a device for performing a convolution neural network forward operation according to an example of the present disclosure.

FIG. 1H is a block diagram of a process of a single-layer convolution neural network forward operation according to an example of the present disclosure.

FIG. 2 is a flowchart of an information processing method according to an example of the present disclosure.

FIG. 3 is a schematic diagram of calling an operation instruction based on single-thread according to an example of the present disclosure.

FIG. 4 is a schematic diagram of calling an operation instruction based on multiple threads according to an example of the present disclosure.

FIG. 5 is a structural diagram of another computation device according to an example of the present disclosure.

DETAILED DESCRIPTION OF THE EXAMPLES

Technical solutions in examples of the present disclosure will be described clearly and completely hereinafter with reference to the accompanied drawings in the examples of the present disclosure. Obviously, the examples to be described are merely some rather than all examples of the present disclosure. All other examples obtained by those of ordinary skill in the art based on the examples of the present disclosure without creative efforts shall fall within the protection scope of the present disclosure.

Terms such as “first”, “second”, “third”, and “fourth” in the specification, the claims, and the drawings are used for distinguishing different objects rather than describing a specific order. In addition, terms such as “include”, “have”, and any variant thereof are used for indicating non-exclusive inclusion. For instance, a process, a method, a system, a product, or an equipment including a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to the process, the method, the product, or the equipment.

Reference to “example” means that a particular feature, a structure, or a characteristic described in conjunction with the example may be included in at least one example of the present disclosure. The term used in various places in the specification does not necessarily refer to the same example, nor does it refer to an example that is mutually exclusive, independent, or alternative to other examples. It can be explicitly and implicitly understood by those skilled in the art that the examples described herein may be combined with other examples.

First, a computation device used in the present disclosure is introduced. FIG. 1A provides a computation device, where the device includes a storage medium 611 (optional), a register unit 612, an interconnection module 613, an operation unit 614, a control unit 615, and a data access unit 616, where

the operation unit 614 include at least two of the following: an addition arithmetic unit, a multiplication arithmetic unit, a comparator, and an activation arithmetic unit.

The interconnection module 613 is configured to control a connection relationship of the arithmetic units in the operation unit 614 so that the at least two arithmetic units form a different computation topology.

The instruction storage unit (which may be a register unit, an instruction cache, or a scratchpad memory) 612 is configured to store the operation instruction, an address of a data block in the storage medium, and a computation topology corresponding to the operation instruction.

The operation instruction may include an operation field and an opcode. Taking a convolution operation instruction as an example, as shown in Table 1, register 0, register 1, register 2, register 3, and register 4 may be operation fields. Each of the register 0, register 1, register 2, register 3, and register 4 may be one or a plurality of registers.

Opcode Register 0 Register 1 Register 2 Register 3 Register 4 COMPUTE Input data Input data Convolution kernel Convolution Address of an starting address length starting address kernel length activation function interpolation table IO Address of an Data length Address of an external memory internal memory of data of data NOP JUMP Target address MOVE Input address Data size Output address

The storage medium 611 may be an off-chip memory, and in certain applications, may also be an on-chip memory for storing a data block. The data block may be n-dimensional data, where n is an integer greater than or equal to 1. For instance, when n=1, the data is one-dimensional data, which is a vector; when n=2, the data is two-dimensional data, which is a matrix; and when n is equal to or greater than 3, the data is multi-dimensional data.

The control unit 615 is configured to fetch an operation instruction, an operation field corresponding to the operation instruction, and a first computation topology corresponding to the operation instruction from the register unit 612, and decode the operation instruction into an execution instruction. The execution instruction is configured to control the operation unit to perform an operation, transfer the operation field to the data access unit 616, and transfer the computation topology to the interconnection module 613.

The data access unit 616 is configured to fetch a data block corresponding to the operation field from the storage medium 611 and transfer the data block to the interconnection module 613.

The interconnection module 613 is configured to receive the first computation topology and the data block. In an example, the interconnection module 613 is further configured to rearrange the data block according to the first computation topology.

The operation unit 614 is configured to call an arithmetic unit of the operation unit 614 according to the execution instruction to perform an operation on the data block to obtain an operation result, transfer the operation result to the data access unit, and store the result in the storage medium. In an example, the operation unit 614 is configured to call an arithmetic unit according to the first computation topology and the execution instruction to perform an operation on the rearranged data block to obtain an operation result, transfer the operation result to the data access unit, and store the result in the storage medium.

In another example, the interconnection module 613 is configured to form the first computation topology according to the connection relationships of the arithmetic units in the operation unit 614.

An interconnection module is set in the computation device provided by the present disclosure. The interconnecting module can connect the arithmetic units in the computation unit to obtain a computation topology corresponding to the computation instruction according to the needs of the computation instruction, so that there is no need to store or fetch intermediate data of the computation in subsequent operations of the operation unit. Through this structure, a single instruction can implement a single input and perform operations of a plurality of arithmetic units to obtain a computation result, which improves the computation efficiency.

A computation method of the computation device shown in FIG. 1A is explained below based on different operation instructions. As an instance, the operation instruction may be a convolution operation instruction. The convolution operation instruction can be applied to a neural network, so the convolution operation instruction may also be called a convolution neural network operation instruction. A formula to be perform by the convolution operation instruction may be: s=s(Σwx_(i)+b), which is to multiply a convolution kernel w by input data x_(i), find the sum, add a bias b, and then perform an activation operation s(h) to obtain a final output result s. According to the formula, the computation topology may be obtained, which is: the multiplication arithmetic unit—the addition arithmetic unit—the (optional) activation arithmetic unit.

A method of performing a convolution operation instruction by the computation device shown in FIG. 1A may include:

fetching, by the control unit 615, a convolution operation instruction, an operation field corresponding to the convolution operation instruction, and the first computation topology (the multiplication arithmetic unit—the addition arithmetic unit—the addition arithmetic unit—the activation arithmetic unit) corresponding to the convolution operation instruction from the register unit 612; transferring, by the control unit, the operation field to a data access unit, and transferring the first computation topology to the interconnection module;

fetching, by the data access unit, a convolution kernel w and a bias b (if b is 0, there is no need to fetch the bias b) corresponding to the operation field from the storage medium, and transferring the convolution kernel w and the bias b to the operation unit; and

multiplying, by the multiplication arithmetic unit of the computation unit, a convolution kernel w and input data Xi to obtain a first result, inputting the first result to the addition arithmetic unit to perform addition to obtain a second result, adding the second result and a bias b to obtain a third result, inputting the third result to the activation arithmetic unit to perform an activation operation to obtain an output result s, transferring the output result s to the data access unit, and storing, by the data access unit, the output result in the storage medium. After each step, the result may be transferred to the data access and stored in storage medium without performing a following step. The step of adding the second result and the bias b to obtain the third result is optional, which means this step is not required when b is 0.

In addition, the order of addition and multiplication can be reversed.

The technical solution provided by the present disclosure can realize convolution operations according to one instruction which is a convolution operation instruction. There is no need to store or obtain intermediate data of convolution operations (such as a first result, a second result, and a third result). The technical solution may reduce the storing and obtaining operations of intermediate data, and may have technical effects of reducing a corresponding operation step and improving outcomes of convolution operations.

It should be understood that the instruction set used in the present disclosure may include one or a plurality of operation instructions. The operation instruction includes, but is not limited to a COMPUTE instruction (an operation instruction), a CONFIG instruction, an IO instruction, an NOP instruction, a JUMP instruction, a MOVE instruction, etc. The COMPUTE instruction includes, but is not limited to, a convolution (CONV) instruction, a pooling operation instruction, etc. Specifically, an executable computation instruction in the present disclosure includes:

a convolution operation instruction. In an example, the convolution COMPUTE instruction (the CONV instruction) includes:

a convolutional neural network sigmoid instruction: according to the instruction, a device fetches input data and a convolution kernel of a specified size from a specified address in a memory (optionally a scratchpad memory or a scalar register file), performs a convolution operation in a convolution operation component, and optionally, performs sigmoid activation on an output result;

a convolutional neural network TanH instruction: according to the instruction, the device fetches input data and a convolution kernel of a specified size from a specified address in a memory (optionally a scratchpad memory) respectively, performs a convolution operation in the convolution operation component, and then performs TanH activation on an output result;

a convolution neural network ReLU instruction: according to the instruction, the device fetches input data and a convolution kernel of a specified size from a specified address in the memory (optionally a scratchpad memory) respectively, performs a convolution operation in a convolution operation component, and then performs ReLU activation on an output result; and

a convolution neural network group instruction: according to the instruction, the device fetches input data and a convolution kernel of a specified size from a specified address in the memory (optionally a scratchpad memory) respectively, partitions the input data and the convolution kernel into groups, performs a convolution operation in a convolution operation component, and then performs activation on an output result.

A convolution operation instruction (pure convolution operation instruction): according to the instruction, the device fetches input data and a convolution kernel of a specified size from a specified address in the memory (optionally a scratchpad memory) respectively, and performs a convolution operation in a convolution operation component. The above-mentioned specified size may be set by the user or manufacturer. For instance, in a computation device of a first manufacturer, the specified size may be set to data of A bit, and in a computation device of a second manufacturer, the specified size may be set to data of B bit. The data of A bit and the data of B bit have different sizes.

The pooling instruction. In an example, the pooling COMPUTE instruction (the pooling operation instruction, which is also referred to as the pooling instruction in the present disclosure) specifically includes:

a Maxpooling forward operation instruction: according to the instruction, the device fetches input data of a specified size from a specified address in a memory (optionally a scratchpad memory or a scalar register file), performs a Maxpooling forward operation in a pooling operation component, and writes a result back to a specified address in the memory (optionally a scratchpad memory or a scalar register file);

a Maxpooling backward training instruction: according to the instruction, the device fetches input data of a specified size from a specified address in a memory (optionally a scratchpad memory or a scalar register file), performs Maxpooling backward training in a pooling operation component, and writes a result back to a specified address in the memory (optionally a scratchpad memory or a scalar register file);

an Avgpooling forward operation instruction: according to the instruction, the device fetches input data of a specified size from a specified address in a memory (optionally a scratchpad memory or a scalar register file), performs an Avgpooling forward operation in a pooling operation component, and writes a result back to a specified address in the memory (optionally a scratchpad memory or a scalar register file);

an Avgpooling backward training instruction: according to the instruction, the device fetches input data of a specified size from a specified address in a memory (optionally a scratchpad memory or a scalar register file), performs Avgpooling backward training in a pooling operation component, and writes a result back to a specified address in the memory (optionally a scratchpad memory or a scalar register file);

a Minpooling forward operation instruction: according to the instruction, the device fetches input data of a specified size from a specified address in a memory (optionally a scratchpad memory or a scalar register file), performs a Minpooling forward operation in a pooling operation component, and writes a result back to a specified address in the memory (optionally a scratchpad memory or a scalar register file); and a Minpooling backward training instruction: according to the instruction, the device fetches input data of a specified size from a specified address in a memory (optionally a scratchpad memory or a scalar register file), performs Minpooling backward training in a pooling operation component, and writes a result back to a specified address in the memory (optionally a scratchpad memory or a scalar register file).

A batch normalization instruction can be used for a batch normalization computation.

A fully connected instruction may include a fully connected layer forward operation instruction.

A fully connected layer forward operation instruction: according to the instruction, a device fetches weight data and bias data from a specified address in a memory, performs a full connection operation in a computation unit, and writes a result back to a specified address in a scratchpad memory.

The CONFIG instruction configures various constants required by a computation of a current artificial neural network layer before the computation starts. For instance, 1/kernel_area can be obtained by configuration using the CONFIG instruction. In the batch normalization computation, the CONFIG instruction configures various constants required for a current layer before a batch normalization computation begins.

The IO instruction is for reading-in input data required for a computation from an external storage space, and storing data to the external space after the computation finishes.

The NOP instruction is for emptying control signals in all control signal cache queues in the current device, and ensuring that all instructions before the NOP instruction are finished. The NOP instruction itself does not include any operations.

The JUMP instruction is for controlling jumping of a next instruction address to be read from an instruction storage unit, so that the jumping of a control flow can be realized.

The MOVE instruction is for moving data of an address in an internal address space of the device to another address in the internal address space of the device. This process is independent of an operation unit and does not occupy resources of the operation unit during execution.

Optionally, operation instructions that can be executed by the computation device may further include:

a Matrix Mult Vector (MMV) instruction: according to the instruction, the device fetches matrix data and vector data of a set length from a specified address in a scratchpad memory, performs a matrix-multiply-vector operation in the operation unit, and writes a computation result back to a specified address in the scratchpad memory; it is worth noting that a vector can be stored in the scratchpad memory as a matrix of a special form (a matrix with only one row of elements);

a Vector Mult Matrix (VMM) instruction: according to the instruction, the device fetches vector data and matrix data of a set length from a specified address in a scratchpad memory, performs a vector-multiply-matrix operation in the operation unit, and writes a computation result back to a specified address in the scratchpad memory; it is worth noting that a vector can be stored in the scratchpad memory as a matrix of a special form (a matrix with only one row of elements);

a Matrix Mult Scalar (VMS) instruction: according from instruction, the device fetches matrix data of a set length from a specified address in a scratchpad memory, fetches matrix data of a specified size from a specified address of a scalar register file, and performs a scalar-multiply-matrix operation in the operation unit, and writes a computation result back to a specified address in the scratchpad memory; it is worth noting that the scalar register file stores not only an address of the matrix but also scalar data;

a Tensor Operation (TENS) instruction: according to the instruction, the device fetches two pieces of matrix data of a set length from two specified addresses in a scratchpad memory, performs a tensor operation on the two pieces of matrix data in the operation unit, and writes a computation result back to a specified address of the scratchpad memory;

a Matrix Add Matrix (MA) instruction: according to the instruction, the device fetches two pieces of matrix data of a set length from two specified addresses in a scratchpad memory, adds the two pieces of matrix data in the operation unit, and writes a result back to a specified address in the scratchpad memory;

a Matrix Sub Matrix (MS) instruction: according to the instruction, the device fetches two pieces of matrix data of a set length from two specified addresses in a scratchpad memory, performs a subtraction operation on the two pieces of matrix data in the operation unit, and writes a computation result back to a specified address in the scratchpad memory;

a Matrix Retrieval (MR) instruction: according to the instruction, the device fetches vector data of a set length from a specified address in a scratchpad memory, fetches matrix data of a specified size from a specified address in the scratchpad memory; in the operation unit, the vector is an index vector, and an i^(th) element of an output vector is a number obtained from an i^(th) column of the matrix by using an i^(th) element of the index vector as an index; and the output vector is written back to a specified address in the scratchpad memory;

a Matrix Load (ML) instruction: according to the instruction, the device loads data of a set length from a specified external source address to a specified address in a scratchpad memory;

a Matrix Store (MS) instruction: according to the instruction, the device stores matrix data of a set length from a specified address in a scratchpad memory to an external target address;

a Matrix Move (MMOVE) instruction: according to the instruction, the device moves matrix data of a set length from a specified address in a scratchpad memory to another specified address in the scratchpad memory;

a Vector-Inner-Product instruction (VP): according to the instruction, the device fetches vector data of a specified size from a specified address in a memory (optionally a scratchpad memory or a scalar register file), performs an inner product (a scalar) on two vectors in a vector computation unit, and writes the result back; optionally, the result is written back to a specified address in the memory (optionally a scratchpad memory or a scalar register file);

a vector cross product instruction (TENS): according to the instruction, the device fetches vector data of a specified size from a specified address in a memory (optionally a scratchpad memory or a scalar register file), performs an inner product (a scalar) on two vectors in a vector computation unit, and writes the result back; optionally, the result is written back to a specified address in the memory (optionally a scratchpad memory or a scalar register file);

a vector elementary arithmetic operation including a Vector-Add-Scalar instruction (VAS): according to the instruction, the device fetches vector data of a specified size from a specified address in a memory (optionally a scratchpad memory or a scalar register file), fetches scalar data from a specified address of a scalar register file of the memory, adds the scalar to each element of the vector in a scalar computation unit, and writes the result back; optionally, the result is written back to a specified address in the memory (optionally a scratchpad memory or a scalar register file);

a Scalar-Sub-Vector instruction (SSV): according to the instruction, the device fetches scalar data from a specified address in the scalar register in a memory (optionally a scratchpad memory or a scalar register file), fetches vector data from a specified address in the memory (optionally the scratchpad memory or the scalar register file), subtracts corresponding elements of the vector from the scalar in a vector computation unit, and writes the result back; optionally, the result is written back to a specified address in the memory (optionally a scratchpad memory or a scalar register file);

a Vector-Dev-Vector instruction (VD): according to the instruction, the device fetches vector data of a specified size from a specified address in a memory (optionally a scratchpad memory or a scalar register file), performs an element-wise division of two vectors in a vector computation unit, and writes the result back; optionally, the result is written back to a specified address in the memory (optionally a scratchpad memory or a scalar register file);

a Scalar-Dev-Vector instruction (SDV): according to the instruction, the device fetches scalar data from a specified address in the scalar register file of a memory (optionally a scratchpad memory or a scalar register file), fetches vector data of a specified size from a specified address in the memory (optionally the scratchpad memory), divides the scalar by corresponding elements in the vector in a vector computation unit, and writes the result back; optionally, the result is written back to a specified position in the memory (optionally a scratchpad memory or a scalar register file).

The computation device can also execute a vector logic instruction, including:

a Vector-AND-Vector instruction (VAV): according to the instruction, the device fetches vector data of a specified size from a specified address in a memory (optionally a scratchpad memory or a scalar register file) respectively, performs an element-wise AND on two vectors in a vector computation unit, and writes the result back; optionally, the result is written back to a specified address in the memory (optionally a scratchpad memory or a scalar register file);

a Vector-AND instruction (VAND): according to the instruction, the device fetches vector data of a specified size from a specified address in a memory (optionally a scratchpad memory or a scalar register file), performs an element-wise AND operation on two vectors in a vector computation unit, and writes the result back; optionally, the result is written back to a specified address in the scalar register file of the memory (optionally a scratchpad memory or a scalar register file);

a Vector-OR-Vector instruction (VOV): according to the instruction, the device fetches vector data of a specified size from a specified address in a memory (optionally a scratchpad memory) respectively, performs an element-wise OR operation on two vectors in a vector computation unit, and writes the result back; optionally, the result is written back to a specified address in the memory (optionally a scratchpad memory or a scalar register file);

a Vector-OR instruction (VOR): according to the instruction, the device fetches vector data of a specified size from a specified address in a memory (optionally a scratchpad memory or a scalar register file), performs an OR operation on each element of the vector in a vector computation unit, and writes the result back; optionally, the result is written back to a specified address in the scalar register file of the memory (optionally a scratchpad memory or a scalar register file);

a transcendental function instruction: according to the instruction, the device fetches vector data of a specified size from a specified address in a memory (optionally a scratchpad memory or a scalar register file), performs a transcendental function operation on the vector data in an operation unit, and writes the result back; optionally, the result is written back to a specified address in a storage unit of the memory (optionally a scratchpad memory or a scalar register file).

The computation device can also execute a vector comparison operation instruction, including:

a Greater-Equal operation instruction (GE): according to the instruction, the device may obtain parameters of the instruction, including a length of a vector, a starting address of two vectors, and a storage address of an output vector, directly from the instruction or by accessing the serial number of the register of a memory (optionally a scratchpad memory or a scalar register file) provided by the instruction, then read data of the two vectors, and compare the elements at all positions in the vectors in a vector comparison operation unit; at the position of a row, if the value of a previous vector is greater than or equal to the value of a subsequent vector, the value of the comparison result vector at that position is set to 1, otherwise it is set to 0; finally, the comparison result is written back to a specified storage address in the memory (optionally the scratchpad memory or the scalar register file);

a Less-Equal operation instruction (LE): according to the instruction, the device may obtain the parameters of the instruction, including the length of a vector, the starting address of the two vectors, and the storage address of the output vector, directly from the instruction or by accessing the serial number of the register of a memory (optionally a scratchpad memory or a scalar register file) provided by the instruction, then read the data of the two vectors, and compare the elements at all positions in the vectors in a vector comparison operation unit; at the position of a row, if the value of a previous vector is less than or equal to the value of a subsequent vector, the value of the comparison result vector at that position is set to 1, otherwise it is set to 0; finally, the comparison result is written back to a specified storage address in the memory (optionally the scratchpad memory or the scalar register file);

a Greater-Than operation instruction (GT): according to the instruction, the device may obtain the parameters of the instruction, including the length of a vector, the starting address of the two vectors, and the storage address of the output vector, directly from the instruction or by accessing the serial number of the register of a memory (optionally a scratchpad memory or a scalar register file) provided by the instruction, then read the data of the two vectors, and compare the elements at all positions in the vectors in a vector comparison operation unit; at the position of a row, if the value of a previous vector is greater than the value of a subsequent vector, the value of the comparison result vector at that position is set to 1, otherwise it is set to 0; finally, the comparison result is written back to a specified storage address in the memory (optionally the scratchpad memory or the scalar register file);

a Less-Than operation instruction (LT): according to the instruction, the device may obtain the parameters of the instruction, including the length of a vector, the starting address of the two vectors, and the storage address of the output vector, directly from the instruction or by accessing the serial number of the register of a memory (optionally a scratchpad memory or a scalar register file) provided by the instruction, then read the data of the two vectors, and compare the elements at all positions in the vectors in a vector comparison operation unit; at the position of a row, if the value of a previous vector is less than the value of a subsequent vector, the value of the comparison result vector at that position is set to 1, otherwise it is set to 0; finally, the comparison result is written back to a specified storage address in the memory (optionally the scratchpad memory or the scalar register file);

an Equal operation instruction (EQ): according to the instruction, the device may obtain the parameters of the instruction, including the length of a vector, the starting address of the two vectors, and the storage address of the output vector, directly from the instruction or by accessing the serial number of the register of a memory (optionally a scratchpad memory or a scalar register file) provided by the instruction, then read the data of the two vectors, and compare the elements at all positions in the vectors in a vector comparison operation unit; at the position of a row, if the value of a previous vector is equal to the value of a subsequent vector, the value of the comparison result vector at that position is set to 1, otherwise it is set to 0; finally, the comparison result is written back to a specified storage address in the memory (optionally the scratchpad memory or the scalar register file);

an Unequal operation instruction (UEQ): according to the instruction, the device may obtain the parameters of the instruction, including the length of a vector, the starting address of the two vectors, and the storage address of the output vector, directly from the instruction or by accessing the serial number of the register of a memory (optionally a scratchpad memory or a scalar register file) provided by the instruction, then read the data of the two vectors, and compare the elements at all positions in the vectors in a vector comparison operation unit; at the position of a row, if the value of a previous vector is unequal to the value of a subsequent vector, the value of the comparison result vector at that position is set to 1, otherwise it is set to 0; finally, the comparison result is written back to a specified storage address in the memory (optionally the scratchpad memory or the scalar register file);

a Vector Max instruction (VMAX): according to the instruction, the device fetches vector data of a specified size from a specified address in a scratchpad memory of a memory (optionally a scratchpad memory or a scalar register file), selects a largest element from the vector data as a result, and writes the result back; optionally, the result is written back to a specified address in the scalar register file of the memory (optionally a scratchpad memory or a scalar register file);

a Vector Min instruction (VMIN): according to the instruction, the device fetches vector data of a specified size from a specified address in a memory (optionally a scratchpad memory or a scalar register file), selects a minimum element from the vector data as a result, and writes the result back; optionally, the result is written back to a specified address in the scalar register file of the memory (optionally a scratchpad memory or a scalar register file);

a Cyclic Shift operation instruction: according to the instruction, the device may obtain parameters of the instruction directly from the instruction or by accessing the serial number of the register of a memory (optionally a scratchpad memory or a scalar register file) provided by the instruction, then cyclically shift vectors in a vector shift unit (which may be a separate vector shift unit or a computation unit), and then write the result of the shift back to a specified storage address in the memory (optionally the scratchpad memory or the scalar register file), where a format of the cyclic shift operation instruction format may include four operation fields, a starting address and length of a vector, a shift stride, and a storage address of an output vector; and

a Random-Vector generation instruction: according to the instruction, the device reads one or more randomly distributed parameters, and the size and storage address of a random vector to be generated from the instruction or from the register file of a memory (optionally a scratchpad memory or a scalar register file), generates the random vector that is in line with the random distribution in a random vector generation unit, and then writes the result of the random vector back to the specified storage address in the memory (optionally the scratchpad memory or the scalar register file).

The Random-Vector generation instruction may be:

a Uniform distribution instruction (UNIF): according to the instruction, the device reads uniformly distributed upper and lower bound parameters, and the size and storage address of the random vector to be generated from the instruction or from the register of a memory (optionally a scratchpad memory or a scalar register file), generates the random vector that is in line with the uniform distribution in a random vector generation unit, and then writes the result of the random vector back to the specified storage address in the memory (optionally the scratchpad memory or the scalar register file); and

a Gaussian distribution instruction (GAUS): according to the instruction, the device reads Gaussian distributed mean and variance parameters, and the size and storage address of the random vector to be generated from the instruction or from the register of a memory (optionally a scratchpad memory or a scalar register file), generates the random vector that is in line with the Gaussian distribution in a random vector generation unit, and then writes the result of the random vector back to the specified storage address in the memory (optionally the scratchpad memory or the scalar register file).

When the computation device shown in FIG. 1A is used to execute a convolutional neural network algorithm (a convolution operation instruction), please refer to the flowchart of the convolutional neural network algorithm shown in FIG. 1B. As shown in FIG. 1B, a convolutional neural network includes output data, an activation function, an input data layer, and a convolution kernel.

Each computation process includes: selecting corresponding input data x_(i) in the input data layer according to a convolution window, and then performing an addition operation on the input data and the convolution kernel. A computation process of the output data is s=s(Σwx_(i)+b), which is to multiply a convolution kernel w by input data x_(i), find the sum, add a bias b, and then perform an activation operation s(h) to obtain a final output data s. The multiplication of the convolution kernel and the input data is vector multiplication.

According to the size k_(x) of the convolution kernel on an X axis and the size k_(y) of the convolution kernel on the Y axis, the convolution window firstly selects input data of which the size is the same as that of the convolution kernel from the input data of which the size of the X axis is W and the size of the Y axis is H, performs horizontal translation and then vertical translation according to translation position vectors S_(x) and S_(y) of the convolution window, and traverses all the input data.

FIG. 1C shows a format of an instruction set according to an example of the present disclosure. As shown in the figure, a convolution neural network operation instruction includes at least one opcode and at least one operation field. The opcode is for indicating a function of the convolution neural network operation instruction. A convolution neural network operation unit can perform a convolution neural network operation by identifying the opcode. The operation field is for indicating data information of the convolution neural network operation instruction. The data information may be an immediate operand or a register number (which, optionally, may be a register file), which includes a starting address and a length of input data, a starting address and a length of the convolution kernel, and a type of an activation function.

The instruction set includes: convolutional neural network COMPUTE instruction with different functions, a CONFIG instruction, an IO instruction, an NOP instruction, a JUMP instruction, and a MOVE instruction. The above operation instructions will not be further described herein. For details, please refer to related descriptions in the above examples.

Optionally, the instruction set may further include a convolution activation CONV_ACTIVATE instruction.

The convolution activation CONV_ACTIVATE instruction: according to the instruction, the device fetches input data and a convolution kernel of a specified size from a specified address in the scratchpad memory (optionally), performs a convolution operation in a convolution operation component, and then performs an activation function operation on an output result; the above-mentioned specified size may be set by the manufacturer or user.

In one example, the CONV_ACTIVATE instruction includes: a convolution operation instruction and an activation instruction. The activation instruction is configured to perform an activation function operation, and the convolution operation instruction is configured to perform a convolution operation. For details, please refer to related descriptions in the above examples.

FIG. 1D is a schematic structural diagram of a device for performing a convolutional neural network forward operation according to an example of the present disclosure. As shown in FIG. 3, the device includes an instruction storage unit 1, a controller unit 2, a data access unit 3, an interconnection module 4, a primary operation module 5, and a plurality of secondary operation modules 6. The instruction storage unit 1, the controller unit 2, the data access unit 3, the interconnection module 4, the primary operation module 5, and the plurality of secondary operation modules 6 may all be realized in a form of a hardware circuit (for instance, including but not limited to FPGA, CGRA, ASIC, analog circuit, memristor, etc.).

The instruction storage unit 1 is configured to read an instruction through the data access unit 3 and store the instruction.

The controller unit 2 is configured to read an instruction from the instruction storage unit 1, decode the instruction into a control signal for controlling the behavior of other modules, and send the control signal to other modules such as the data access unit 3, the primary operation module 5, and the plurality of secondary operation modules 6.

The data access unit 3 can access an external address space, directly read and write data to each storage unit inside the device to complete the loading and storage of the data.

The interconnection module 4 is configured to connect the primary operation module and the secondary operation modules, and can be implemented into different interconnection topologies (such as tree structure, ring structure, grid structure, hierarchical interconnection, bus structure, etc.).

FIG. 1E schematically shows an implementation of the interconnection module 4: an H-tree module. The interconnection module 4 forms a data path between the primary operation module 5 and the plurality of secondary operation modules 6, where the data path is a binary tree path composed of a plurality of nodes. Each node can transfer data received from an upstream node to two downstream nodes, and merge data returned by the two downstream nodes and return to an upstream node. For instance, at the beginning of a computational phase of a convolutional neural network, neuron data in the primary operation module 5 is sent to each secondary operation module 6 through the interconnection module 4; when the secondary operation modules 6 finish computing, neuron values output by the respective secondary operation modules are spliced stage-by-stage into a complete vector composed of neurons in the interconnection module. For instance, if there are N secondary operation modules in the device, input data x_(i) is transferred to the N secondary operation modules and each of the secondary operation modules performs a convolution operation on the input data x_(i) and the convolution kernel corresponding to the secondary operation module to obtain scalar data. The scalar data of the respective secondary operation modules are merged into an intermediate vector including N elements by the interconnection module 4. If the convolution window obtains a total of A*B pieces of (A pieces in the X direction, B pieces in the Y direction, where X and Y are coordinate axes of the three-dimensional orthogonal coordinate system) input data x_(i) by traverse, a convolution operation is perform on the above A*B pieces of x_(i) and all the vectors obtained are merged in the primary operation module to obtain a three-dimensional intermediate result of A*B*N.

FIG. 1F is a block diagram of a structure of the primary operation module 5 of a device for performing a convolutional neural network forward operation according to an example of the present disclosure. As shown in FIG. 1F, the primary operation module 5 includes a first operation unit 51, a first data dependency determination unit 52, and a first storage unit 53.

The first operation unit 51 includes a vector addition unit 511 and an activation unit 512. The first operation unit 51 is configured to receive a control signal from the controller unit and complete various operational functions of the primary operation module 5. The vector addition unit 511 is configured to perform an operation of adding a bias in the forward computation of the convolutional neural network, and perform element-wise addition on bias data and the intermediate results to obtain a bias result. The activation operation unit 512 performs an activation function operation on the bias result. The bias data may be read in from an external address space, or may be stored locally.

The data dependency determination unit 52 is a port for the first operation unit 51 to read/write the first storage unit 53, so as to ensure consistency in reading data from and writing data to the first storage unit 53. At the same time, the first data dependency determination unit 52 is also configured to send data read from the first storage unit 53 to the secondary operation modules through the interconnection module 4. Output data of the secondary operation modules 6 is directly sent to the first operation unit 51 through the interconnection module 4. An instruction output by the controller unit 2 is sent to the operation unit 51 and the first data dependency determination unit 52 to control their behavior.

The storage unit 53 is configured to cache input data and output data used by the primary operation module 5 during a computation process.

FIG. 1G is a block diagram of a structure of the secondary operation modules 6 of a device for performing a convolutional neural network forward operation according to an example of the present disclosure. As shown in FIG. 1E, each secondary operation module 6 includes a second operation unit 61, a data dependency determination unit 62, a second storage unit 63, and a third storage unit 64.

The second operation unit 61 is configured to receive a control signal from the controller unit 2 and perform a convolution operation. The second operation unit includes a vector multiplication unit 611 and an accumulation unit 612, which are respectively responsible for a vector multiplication operation and an accumulation operation in a convolution operation.

The second data dependency determination unit 62 is responsible for reading and writing the second storage unit 63 during a computation process. Before performing read/write operations, the second data dependency determination unit 62 first ensures that there is no consistency conflict between the reading and writing of data used by instructions. For instance, all control signals sent to the data dependency unit 62 are stored in the instruction queue inside the data dependency unit 62. In this queue, if a range of data to be read by a reading instruction conflicts with a range of data to be written by a writing instruction that is located at the front of the queue, the instruction can only be executed until a writing instruction depended by the instruction has been executed.

The second storage unit 63 is configured to cache input data and output scalar data of the secondary operation modules 6.

The third storage unit 64 is configured to cache convolution kernel data required by the secondary operation modules 6 in a computation process.

FIG. 1H is a flowchart of executing a convolutional neural network by a convolution neural network operation device according to an example of the present disclosure. As shown in FIG. 1H, a process of executing the convolutional neural network neural network instruction includes:

a step S1, pre-storing an IO instruction in a starting address of the instruction storage unit 1;

a step S2, the operation starts, reading, by the controller unit 2, the IO instruction from the starting address of the instruction storage unit 1, and according to a control signal decoded from the instruction, reading, by the data access unit 3, all corresponding convolutional neural network operation instructions from an external address space, and caching the instructions in the instruction storage unit 1;

a step S3, reading, by the controller unit 2, a next IO instruction from the instruction storage unit, and according to a control signal obtained by decoding, reading, by the data access unit 3, all data (such as input data, an interpolation table for a quick activation function operation, a constant table for configuring parameters of the operation device, bias data, etc.) required by the primary operation module 5 from the external address space to the first storage unit 53 of the primary operation module 5;

a step S4, reading, by the controller unit 2, a next IO instruction from the instruction storage unit, and according to a control signal decoded from the instruction, reading, by the data access unit 3, convolution kernel data required by the secondary operation modules 6 from the external address space;

a step S5, reading, by the controller unit 2, a next CONFIG instruction from the instruction storage unit, and according to a control signal obtained by decoding, configuring, by the device, various constants required by the computation of the neural network layer;

for instance, the first operation unit 51 and the second operation unit 61 may configure a value of an internal register of the parameter configuration unit in the control signal, where the parameter includes, for instance, data required by an activation function;

a step S6, reading, by the controller unit 2, a next COMPUTE instruction from the instruction storage unit, and according to a control signal decoded from the instruction, sending, by the primary operation module 5, input data in a convolution window to each secondary operation module 6 through an interconnection module 4 and saving the input data to the second storage unit 63 of the secondary operation module 6; and then moving the convolution window according to the instruction;

a step S7, according to the control signal decoded from the COMPUTE instruction, reading, by the operation unit 61 of the secondary operation module 6, the convolution kernel from the third storage unit 64; reading the input data from the second storage unit 63 to complete the convolution operation of the input data and the convolution kernel; and returning an obtained intermediate result through the interconnection module 4;

a step S8, in the interconnection module 4, splicing intermediate results returned from respective secondary operation modules 6 stage by stage to obtain a complete intermediate vector;

a step S9, obtaining, by the primary operation module 5, the intermediate vector returned by the interconnection module 4; traversing, by the convolution window, all input data; splicing, by the primary operation module, all returned vectors into an intermediate result; according to the control signal decoded from the COMPUTE instruction, reading bias data from the first storage unit 53, adding the intermediate result and the bias data in a vector addition unit 511 to obtain a bias result; activating the bias result by the activation unit 512, and writing final output data back to the first storage unit; and

a step S10, reading, by the controller unit 2, a next IO instruction from the instruction storage unit, and according to a control signal decoded from the instruction, storing, by the data access unit 3, the output data in the first storage unit 53 to a specified address in the external address space, then the operation finishes.

The implementation of a multi-layer convolution neural network is similar to that of a single-layer convolution neural network. After an upper layer of the convolutional neural network is executed, an operation instruction of a next layer uses an output data address of the upper layer stored in the primary operation unit as an input data address of this layer. Similarly, the address of a convolution kernel and the address of bias data in the instruction may also be changed to an address corresponding to this layer.

The present disclosure uses a device and an instruction set for performing the convolutional neural network forward operation, which solves the problem of the lack of CPU and GPU computation performance, and the problem of high front-end decoding overhead. The present disclosure effectively improves support for the forward operation of a multi-layer convolutional neural network.

By using a dedicated on-chip cache for the forward operation of a multi-layer convolutional neural network, input neurons and convolution kernel data may be fully reused, which may avoid repeated reading of these data from the memory, reduce the memory access bandwidth, and prevent the memory bandwidth from becoming a performance bottleneck of the forward operation of a multi-layer artificial neural network.

Based on the above examples, FIG. 2 shows an information processing method according to an example of the present disclosure. The method shown in FIG. 2 may include:

a step S102, obtaining, by a computation device, a target image to be processed, where the target image includes feature identification objects which are used to identify the target image or identify objects included in the target image.

The feature identification objects may be a target object to be recognized, such as people, commodities, animals, etc.; and may also be an object used to identify the environment in which the target image is located, such as trees, beaches, sunshine, houses, walls, and the like. The count of feature recognition objects and the input format of the target image are not limited herein. For instance, the target image may be an image containing one or more feature identification objects, or frame images in one or more segments of videos containing feature identification objects, etc.

It should be understood that when solutions of the present disclosure is used for object identification and classification, the count of the feature identification objects is preferentially 1; in other words, the target image is preferably a close-up image of the feature identification object. It should be understood that when solutions of the present disclosure is used for scenario identification and classification, the count of the feature identification objects is not limited herein. For instance, if the target image is an image taken by the user in a forest, the feature identification object may be multiple trees/flowers and the like.

The method further includes: a step S104, using, by the computation device, the target image as an input of the operation unit to call the operation instruction to classify feature identification objects in the target image, so as to obtain a target classification result; where

the target classification result is used to indicate target classifications to which the feature identification objects belong, and the operation instruction is a pre-stored instruction for object classification.

The operation instruction includes, but is not limited to, a convolution operation instruction, a pooling instruction, a normalization instruction, a non-linear activation instruction, and the like. For details, please refer to related descriptions in the above examples. Optionally, the process of calling related operation instructions in the computation device (such as an operation unit) to perform object detection and classification processing on the target image will not be further described herein. For details, please refer to the specific descriptions related to instruction calling in the above examples.

Some examples involved in the present disclosure are described below.

In the step S102, the input format of the target image may be an image format such as bmp, gif, jpeg, etc.; or may also be multi-dimensional matrix data converted from pixels of the image.

In an optional example, the step S102 specifically includes: obtaining an original image to be processed input by the user and pre-processing the original image to obtain the target image to be processed, where the pre-processing includes one or more of the following processing: translation, scaling transformation, non-linear transformation, normalization, format conversion, data deduplication, processing of data exception, and data missing filling.

In a specific implementation, the computation device obtains an original image to be processed input by a user. The description of the original image will not be further described herein. For details, please refer to the related description of the target image. Further, the computation device may call a related operation instruction to perform pre-processing, such as normalization, format conversion, color conversion, etc., on the original image to obtain the target image to be processed. The pre-processing includes, but is not limited to, format conversion (such as normalization processing and the like), color conversion (such as converting into a gray-scale image), image restoration, image modification, and other operation processing. Correspondingly, the operation instruction may be an instruction related to the pre-processing. For instance, when the pre-processing is the normalization processing, the corresponding operation instruction is a normalization instruction.

The pre-processing includes, but is not limited to, any one or more of the following: data format conversion (such as normalization, integer data conversion, etc.), data deduplication, data exception processing, filling missing data, color conversion, image restoration, image modification, and the like. For instance, the data format conversion may specifically be: conversion between continuous data and discrete data; power conversion, which is to convert non-power weight data in input data (a multi-dimensional matrix of the target image) of a neural network to power weight data; statistics of floating-point data, which is to count bits of exponent offset and exponent bits required for storing different types of data during a forward operation of the artificial neural network; and floating-point data conversion, which is to convert between a short-bit floating point data type and a long-bit floating point data type, and the like, which is not limited in the present disclosure.

Based on the examples of FIGS. 1A-1H, in the step S102, the computation device can obtain the target image to be processed input by the user. In an optional example, the communication unit may be the storage medium (the off-chip memory) shown in FIG. 1A or may be an input/output (IO) unit, which is not limited herein.

In an optional example, the computation device may be the computation device shown in FIG. 1A or FIG. 1D. Specifically, the computation device can store various operation instructions in the register unit or the instruction storage unit through the data access unit; further, the computation device can read/write and store various operation instructions through the data access unit. The controller unit is configured to control the reading of various operation instructions from the register unit (or the instruction storage unit, etc.) and decode an operation instruction into an executable operation instruction. Optionally, the controller unit may also send the operation instruction to the operation unit for execution. Specifically, related arithmetic units can be called in turn for data processing according to the computation topology corresponding to the operation instruction. A convolution operation instruction is described in details below as an instance. The interconnection module is configured to receive input data (the target image) and a computation topology, where the computation topology is a topology corresponding to the operation instruction. For instance, when the operation instruction is a convolution operation instruction, the corresponding computation topology may be: the multiplication arithmetic unit—the addition arithmetic unit—(optional) the activation arithmetic unit. Each type of arithmetic unit is configured to perform a corresponding computational function operation, for instance, the multiplication arithmetic unit is configured to perform a multiplication operation, etc., which will not be further described in the present disclosure.

Some examples involved in the step S104 are described below.

A specific implementation of the step S104 may be: using, by the computation device, the target image as input of the operation unit to call a first operation instruction to perform feature extraction on the target image, thereby obtaining a feature image; further, calling a second operation instruction to perform object classification processing on the feature image, thereby obtaining a target classification result, where the first operation instruction is a preset instruction for feature extraction, and the second operation instruction is a preset instruction for object classification.

Firstly, some examples involved in the feature extraction are described below.

Specifically, the computation device may call the related first operation instruction in the operation unit to perform feature extraction on the target image to obtain a feature image. It should be understood that when an expression form of the target image is a multi-dimensional matrix, feature extraction of the target image belongs to data dimensionality reduction, which reduces the complexity of data processing, reduces the computation load of the computation device, and improves computation efficiency.

In an optional example, the first operation instruction may be an instruction for feature extraction. For details, please refer to related descriptions in the above examples.

In an optional example, the first operation instruction may include any one or more of the following instructions: a convolution operation instruction, a normalization instruction, a non-linear activation instruction, and a pooling instruction. It should be noted that when there are a plurality of the first operation instructions, which can also be called an operation instruction set, an order, count, and calling thread of the respective first operation instructions in the operation instruction set may be customized by the user side or the computer device side (such as a terminal), which is not limited herein.

FIG. 3 shows a schematic diagram of calling an operation instruction based on single-thread to perform feature extraction. Specifically, the controller unit may extract a convolution operation instruction from the register unit and send the convolution operation instruction to the operation unit to process the target image, thereby obtaining a first intermediate image. Then the controller unit may fetch a normalization instruction from the register unit and send the normalization instruction to the operation unit to process the first intermediate image, thereby obtaining a second intermediate image. The controller unit may fetch a non-linear activation instruction from the register unit and send the non-linear activation instruction to the operation unit to process the second intermediate image, thereby obtaining a third intermediate image. Then the controller unit may fetch a pooling instruction from the register unit and send the pooling instruction to the operation unit to process the third intermediate image, thereby obtaining a feature image after feature extraction.

Optionally, when each operation instruction shown in FIG. 3 is called for execution, the execution order may be changed; for instance, the normalization instruction may be called before the convolution operation instruction, which is not limited herein.

In an optional example, the present disclosure supports multi-thread (multiple pipelines) feature extraction processing. In other words, the feature extraction in the present disclosure may be implemented by threads splitting or merging. Implementations of thread splitting include, but are not limited to, data copying, data grouping, and the like, while implementations of thread merging include, but are not limited to, data addition and subtraction, data multiplication, data combination and arrangement, and the like.

FIG. 4 shows a schematic diagram of calling an operation instruction based on multiple threads to perform feature extraction. Specifically, the computation device may perform data operations of two threads at the same time. The operation instructions to be used in each thread may be the same or different, and an order and a count of calling the operation instructions are not limited herein. As shown in FIG. 4, one of the threads sequentially executes the operation instructions in FIG. 3 twice at the same time, while the other thread sequentially executes the operation instructions in FIG. 3 once.

It should be noted that when multi-thread feature extraction is involved in the present disclosure, the feature image after feature extraction may be obtained by aggregating result data processed by each thread. In other words, the feature image data after the feature extraction may include, but is not limited to, a plurality of pieces of matrix data with the same dimension or different dimensions, which is not limited herein.

In some possible examples, the computation device may also call other instructions related to a feature extraction algorithm to perform feature extraction on the target image, so as to obtain a feature image. The feature extraction algorithm includes, but is not limited to HOG (Histogram of Oriented Gradients), an SIFT feature extraction algorithm, etc., and these feature extraction algorithms are composed of one or more corresponding operation instructions, which are not limited in the present disclosure.

Secondly, some examples involved in the object classification are described below.

Specifically, the computation device may call the second operation instruction to perform object classification on the feature image to obtain a target classification result, the process of which is similar to that of the above feature extraction. The target classification result is used to identify the target image or identify a category to which the feature identification objects in the target image belong. In other words, the computation device may obtain the category to which the feature identification objects belong, such as a category of milk and a category of cup; or the computation device may obtain a category of environment to which the target image corresponds, such as an indoor environment, an outdoor environment, a forest, a beach, and the like, which are not limited in the present disclosure.

In a specific example, the computation device calls the second operation instruction to classify the feature image (feature image data obtained after feature extraction), so as to obtain a plurality of classification results of the target image. Specifically, there may be n classification results, where n is a positive integer. The above process is similar to the above feature extraction process. Optionally, for details of the classification result, please refer to related descriptions of the target classification result. Each classification result has a corresponding score.

Further, the computation device may select a classification result which satisfies a scoring condition from the n classification results as the target classification result. The scoring condition is customized by the user side or the computation device side, such as a highest score, a lowest score, a score exceeding a preset threshold, etc., which is not limited herein. There may be one or more classifications for the target classification results, which is not limited herein.

In an optional example, the computation device may call the second operation instruction to perform object classification on the feature image to obtain corresponding output data (the classification result of the present disclosure), the process of which is similar to that of the above feature extraction. Optionally, after calling the second operation instruction to perform object classification on the feature image, which is similar to the above feature extraction process, the computation device can also call a vector operation instruction to perform a computation of a softmax algorithm on output data to normalize the output data, so that a range of final output data is between 0-1. It can also be understood that the n classification results in the present disclosure can specifically be used to identify data corresponding to the n categories. The data may be data normalized using the softmax algorithm, or data obtained by using an instruction that is similar to the related operation instruction used for the above feature extraction to process the feature image (data), which is not limited in the present disclosure.

It should be noted that a processing similar to the feature extraction described above can be used to perform object classification on the feature image, which is to perform object classification on the feature image based on a single-thread or multi-thread operation instruction set. The operation instruction set may be shown in FIG. 3 and FIG. 4. Optionally, after object classification is performed on the feature image using the operation instruction set of each thread, the fully connected instruction may be further configured to aggregate processing result data of each thread to obtain the classification result for the target image.

In addition, the first operation instruction and the second operation instruction in the present application may be the same or different. For the operation instruction, please refer to the related descriptions in the above examples.

In an optional example, a specific implementation of the step S104 above 4 is briefly described below combined with the above examples.

In a specific implementation, the computation device fetches a corresponding operation instruction from the register unit (or the instruction storage unit) through the controller unit and the data access unit, where the operation instruction is configured to process the target image (which may specifically be object identification processing or scenario identification processing). For the operation instruction, please refer to the related introduction in the above examples; for instance, the instruction may be the operation instruction associated with a network model. The count of the operation instructions is not limited herein.

Further, after the controller unit fetches the operation instruction, the controller unit sends the operation instruction to the operation unit to process the target image in the operation unit according to the computation topology corresponding to the operation instruction, so as to obtain the target classification result.

A specific implementation process of the step S104 is described in detail below with the operation instruction being a convolution operation instruction as an instance.

In a specific implementation, referring to the computation device shown in FIG. 1A, the computation device obtains a target image input by a user through the communication unit (or a storage medium, an off-chip memory). Optionally, the computation device may call a related computation instruction to perform conversion of a preset format on the target image, thereby obtaining image data which can be identified and processed by the computation device, such as a vector composed of i x_(i) pixel data. The preset format is customized by the user side or the computation device side. Further, the computation device fetches a convolution operation instruction from the register unit through the data access unit and the controller unit, and sends the convolution operation instruction to the operation unit for execution, in other words, a formula to be executed is s=s(Σwx_(i)+b). w is convolution kernel, and x_(i) is input data. Correspondingly, the computation device controls the operation unit to execute the convolution operation instruction on the input data x_(i) (first language information). Specifically, the computation device calls the multiplication arithmetic unit in the operation unit to multiply a convolution kernel w by input data x_(i) calls the addition arithmetic unit to find the sum, adds a bias b, and then calls the activation arithmetic unit to perform an activation operation s(h), so as to obtain a final output result s. The output result is the target image or intermediate data. When the output result is intermediate data, according to a similar computation principle of the above convolution operation instruction, the computation device may further call other operation instructions to process the intermediate data. The process is repeated until the target image is obtained.

In another specific implementation, referring to the computation device shown in FIG. 1D, the process is similar to that of the above step S104 and uses the computation device shown in 1D. The operation unit may specifically include a primary operation module, secondary operation modules, and an interconnection module connecting the primary operation module and the secondary operation modules. The interconnection module may be configured to transfer data between the primary operation module and the secondary operation modules, receive a computation topology corresponding to an operation instruction, etc. The computation device may control an implementation of a bias b operation and an activation S (h) operation in the convolution operation in the primary operation module, and control an implementation of a vector multiplication operation wx_(i) and an accumulation operation Σ in the respective secondary operation modules. Specifically, the computation device may transfer input data x_(i) (a target image) to each secondary operation module through the controller unit, so as to first call a multiplication arithmetic unit in each secondary operation module to multiply a convolution kernel w by the input data x_(i), and then call an addition arithmetic unit to sum and obtain an output scalar. Then the interconnection module is configured to accumulate and splice output scalars of the respective secondary operation modules stage by stage into an intermediate vector and send the intermediate vector to the primary operation module. Further, the computation device calls the addition arithmetic unit in the primary operation module to splice intermediate vectors corresponding to all input data into an intermediate result, adds a bias b to the intermediate result, and then calls an activation arithmetic unit to perform an activation operation s(h) to obtain a final output result s.

For the implementation of calling related operation instructions in the computation device to process the first language information, please refer to related descriptions of the above FIGS. 1A to 1H. In other words, the examples of the FIGS. 1A to 1H may also be correspondingly applied to the examples of the information processing method described in FIG. 2, and will not be further described herein. It should be understood that the convolution operation instruction in the above description is only used as an instance to illustrate the convolution operation instruction calling and data processing, which is not a limitation; accordingly, when the operation instruction is another instruction instead of the convolution operation instruction, a related processing method similar to that of the convolution operation instruction may also be used to implement steps of the method examples of the present disclosure.

The examples of the present disclosure may realize intelligent, fast, and accurate object classification and identification, which may have technical effects of lower power consumption and faster speed compared with the prior art in which a general-purpose processor is used for object classification and identification.

FIG. 5 is a structural diagram of a computation device (which may specifically be a terminal device) according to an example of the present disclosure. The computation device shown in FIG. 5 includes a communication unit 617 and an operation unit 614, where

the communication unit 617 is configured to obtain a target image to be processed, where the target image includes feature identification objects which are used to identify the target image or identify objects included in the target image;

the operation unit 614 is configured to use the target image as an input of the operation unit to call the operation instruction to classify feature identification objects in the target image, so as to obtain a target classification result, where

the target classification result is used to indicate target classifications to which the feature identification objects belong, and the operation instruction is a pre-stored instruction for object classification.

Optionally, the computation device further includes a storage medium 611, a register unit 612, an interconnection module 613, a controller unit 615, and a data access unit 616. For the above function units, please refer to related descriptions of the examples in FIG. 1. Optionally, the communication unit and the storage medium may be the same or different. For instance, the communication unit may be a storage medium or be an IO unit of the computation device, which is not limited herein.

In an optional example, the computation device further includes a register unit and a controller unit, where

the controller unit is configured to fetch a first operation instruction and a second operation instruction from the register unit, and send the first operation instruction and the second operation instruction to the operation unit;

the operation unit is configured to call the first operation instruction to perform feature extraction on the target image to obtain a feature image; and

the operation unit is further configured to call the second operation instruction to perform object classification on the feature image to obtain a target classification result, where

the first operation instruction is a preset instruction for feature extraction, and the second operation instruction is a preset instruction for object classification.

In an optional example,

the operation unit to configured to call the second computation instruction to perform object classification on the feature image to obtain a plurality of classification results, where the classification results include classification scores; and

the operation unit is further configured to select a classification result corresponding to a classification score exceeding a preset threshold from the plurality of classification results as the target classification result.

In an optional example,

the operation unit is configured to perform feature extraction on the target image based on an operation instruction set of at least one thread to obtain a feature image, where the operation instruction set includes at least one first operation instruction, and an order of calling the first operation instruction in the operation instruction set is customized by a user side or a terminal side.

In an optional example,

the communication unit is configured to obtain an original image to be processed input by a user,

the operation unit is further configured to pre-process the original image to obtain a target image to be processed, where the pre-processing includes one or more of the following processing: translation, scaling transformation, non-linear transformation, normalization, format conversion, data deduplication, processing of data exception, and data missing filling.

In an optional example, the computation device further includes a data access unit and a storage medium,

the computation device controls the operation unit to send the target classification result to the data access unit and store the same in the storage medium.

In some possible examples, the operation unit includes a primary operation module and a plurality of secondary operation modules, where the primary operation module is interconnected with the plurality of secondary operation modules by an interconnection module, and when the operation instruction is a convolution operation instruction,

the calling the operation instruction to classify the feature identification objects in the target image includes:

controlling, by the computation device, the secondary operation modules to implement a convolution operation of input data and a convolution kernel in a convolution neural network algorithm, where the input data is the target image and the convolution neural network algorithm corresponds to the convolution operation instruction;

controlling, by the computation device, the interconnection module to implement data transfer between the primary operation module and the secondary operation modules; before a forward operation of a neural network fully connected layer starts, transferring, by the primary operation module, the input data to each secondary operating through the interconnection module; and after the computation of the secondary operation modules is completed, splicing, by the interconnection module, output scalars of the respective secondary operation modules stage by stage to obtain an intermediate vector, and sending the intermediate vector back to the primary operation module; and

controlling, by the computation device, the primary operation module to splice intermediate vectors corresponding to all input data into an intermediate result, and performing subsequent operations on the intermediate result.

In an optional example, the performing subsequent operations on the intermediate result includes:

controlling, by the computation device, the primary operation module to add bias data to the intermediate result, and then performing an activation operation.

In an optional example, the primary operation module includes a first operation unit, where the first operation unit includes a vector addition unit and an activation unit,

the steps of controlling, by the computation device, the primary operation module to add bias data to the intermediate result, and then performing an activation operation include:

controlling, by the computation device, the vector addition unit to implement a bias addition operation of a convolutional neural network operation and perform element-wise addition on bias data and the intermediate result to obtain a bias result; and

controlling, by the computation device, the activation unit to perform an activation function operation on the bias result.

In an optional example, the primary operation module includes a first storage unit, a first operation unit, a first data dependency determination unit, and a first storage unit;

the computation device controls the first storage unit to cache input data and output data used by the primary operation module during a computation process, where the output data includes the object classification result;

the computation device controls the first operation unit to perform various operational functions of the primary operation module;

the computation device controls the data dependency determination unit to ensure that there is no consistency conflict in reading data from and writing data to the first storage unit, read an input neuron vector from the first storage unit, and send the vector to the secondary operation modules through the interconnection module; and

an intermediate result vector from the interconnection module is sent to the first operation unit.

In an optional example, each of the secondary operation modules includes a second operation unit, where the second operation unit includes a vector multiplication unit and an accumulation unit, and

the step of controlling, by the computation device, the secondary operation modules to perform a convolution operation of input data and a convolution kernel in a convolution neural network algorithm includes:

controlling, by the computation device, the vector multiplication unit to perform a vector multiplication operation of the convolution operation; and

controlling, by the computation device, the accumulation unit to perform an accumulation operation of the convolution operation.

In an optional example, each secondary operation module includes a second operation unit, a second data dependency determination unit, a second storage unit, and a third storage unit;

the computation device controls the second operation unit to perform various operational logical computations of the secondary operation module;

the computation device controls the second data dependency determination unit to perform a reading/writing operation on the second storage unit and the third storage unit during a computation process and ensure that there is no consistency conflict between the reading and writing operations on the second storage unit and the third storage unit;

the computation device controls the second storage unit to cache input data and an output scalar and an output scalar obtained from the computation performed by the secondary operation modules; and

the computation device controls the third storage unit to cache a convolution kernel required by the secondary operation module during a computation process.

In an optional example, the first data dependency or the second data dependency ensures that there is no consistency conflict in reading and writing in the following manners: storage addresses corresponding to data/instructions stored in the corresponding storage unit do not overlap; or determining whether there is dependency between a control signal that has not been executed and data of a control signal that is being executed, if there is no dependency, the control signal is allowed to be issued immediately, otherwise, the control signal is not allowed to be issued until all control signals on which the control signal is dependent have been executed, where

the computation device controls the controller unit to obtain an operation instruction from the register unit and decode the operation instruction into the control signal for controlling behavior of other modules, where the other modules include the primary operation module and the plurality of secondary operation modules.

In an optional example, the computation device controls the plurality of secondary operation modules to compute respective output scalars in parallel by using the same input data and respective convolution kernels.

In an optional example, an activation function active used by the primary operation module may be any of the following non-linear functions: sigmoid, tanh, relu, softmax, or may be a linear function.

In an optional example, the interconnection module forms a data channel for continuous or discrete data between the primary operation module and the plurality of secondary operation modules. The interconnection module has any of the following structures: a tree structure, a ring structure, a grid structure, a hierarchical interconnection, and a bus structure.

For those parts which are not shown or described in this example of the present disclosure, please refer to related descriptions of the above examples.

An example of the present disclosure further provides a computer storage medium on which a computer program is stored for electronic data exchange. The computer program may cause a computer to perform part or all of the steps of any information processing method described in the foregoing method examples.

An example of the present disclosure further provides a computer program product, where the computer program product includes a non-transitory computer-readable storage medium on which a computer program is stored. The computer program may cause a computer to perform part or all of the steps of any information processing method described in the foregoing method examples.

An example of the present disclosure also provides an acceleration device which includes: a memory which stores executable instructions, and a processor configured to execute the executable instructions in the storage unit according to the information processing method.

The processing unit may be a single processing unit, or may include two or more processing units. In addition, the processor may also include a general-purpose processor (CPU) or a graphics processing unit (GPU), a field programmable gate array (FPGA), or an application-specific integrated circuit (ASIC) to set up and operate a neural network. The processor may also include an on-chip memory for caching (including a memory in the processing device).

In some examples, the present disclosure provides a chip which includes the above neural network processor configured to execute the information processing method.

In some examples, the present disclosure provides a chip package structure which includes the above chip.

In some examples, the present disclosure provides a board card which includes the above chip package structure.

In some examples, the present disclosure provides an electronic device which includes the above board card.

The electronic device may include a data processing device, a robot, a computer, a printer, a scanner, a tablet, a smart terminal, a mobile phone, a traffic recorder, a navigator, a sensor, a webcam, a server, a cloud-based server, a camera, a video camera, a projector, a watch, a headphone, a mobile storage, a wearable device, a vehicle, a household appliance, and/or a medical equipment.

The vehicle may include an airplane, a ship, and/or a car. The household electrical appliance may include a television, an air conditioner, a microwave oven, a refrigerator, an electric rice cooker, a humidifier, a washing machine, an electric lamp, a gas cooker, and a range hood. The medical equipment may include a nuclear magnetic resonance spectrometer, a B-ultrasonic scanner, and/or an electrocardiograph.

It should be noted that, the foregoing examples of method, for the sake of conciseness, are all described as a series of action combinations, but those skilled in the art should know that since according to the present disclosure, the steps may be performed in a different order or simultaneously, the disclosure is not limited by the described order of action. Secondly, those skilled in the art should also understand that the examples described in the specification are all optional, and the actions and modules involved are not necessarily required for this disclosure.

In the examples above, the description of each example has its own emphasis. For a part that is not described in detail in one example, reference may be made to related descriptions in other examples.

It should be understood that in the examples provided by the present disclosure, the disclosed device may be implemented in another manner. For instance, the examples above are merely illustrative. For instance, the division of the units is only a logical function division. In a certain implementation, there may be another manner for division. For instance, a plurality of units or components may be combined or may be integrated in another system, or some features can be ignored or not performed. In addition, the displayed or discussed mutual coupling or direct coupling or communication connection may be implemented through indirect coupling or communication connection of some interfaces, devices or units, and may be electrical or other forms.

The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units. In other words, the components may be located in one place, or may be distributed to a plurality of network units. According to certain needs, some or all of the units can be selected for realizing the purposes of the examples of the present disclosure.

In addition, the functional units in each example of the present application may be integrated into one processing unit, or each of the units may exist separately and physically, or two or more units may be integrated into one unit. The integrated units above may be implemented in the form of hardware or in the form of software program modules.

When the integrated units are implemented in the form of a software program module and sold or used as an independent product, they may be stored in a computer-readable memory. Based on such understanding, the essence of the technical solutions of the present disclosure, or a part of the present disclosure that contributes to the prior art, or all or part of technical solutions, can all or partly embodied in the form of a software product that is stored in a memory. The software product includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the examples of the present disclosure. The foregoing memory includes: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk, or an optical disc, and other media that can store program codes.

A person of ordinary skill in the art may understand that all or part of the steps of the foregoing examples of method may be completed by a program instructing related hardware. The program may be stored in a computer-readable memory, and the memory may include a flash disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, an optical disk, or the like.

The examples of the present disclosure have been described in detail above. Specific examples have been used in the specification to explain the principles and implementation manners of the present disclosure. The descriptions of the above examples are only used to facilitate understanding of the methods and core ideas of the present disclosure. Persons of ordinary skill in the art may change the implementation and application scope according to the ideas of the present application. In summary, the content of this specification should not be construed as a limitation on the present disclosure. 

What is claimed is:
 1. An information processing method applied to a computation circuit, wherein the computation lit comprises a communication circuit and an operation circuit, and the method comprises: controlling, by the computation circuit, the communication circuit to obtain a target image to be processed, wherein the target image includes one or more feature identification objects which are used to identify the target image or identify objects included in the target image; and controlling, by the computation circuit, the operation circuit to obtain an operation instruction and execute the operation instruction to classify the feature identification objects in the target image to obtain a target classification result, wherein the target classification result indicates at least one target classification to which the feature identification objects belong.
 2. The method of claim 1, wherein the computation circuit further includes a register circuit and a controller circuit, and the controlling, by the computation circuit, the operation circuit to obtain an operation instruction and execute the operation instruction to classify feature identification objects in the target image, so as to obtain a target classification result includes: controlling, by the computation circuit, the controller circuit to fetch a first operation instruction and a second operation instruction from the register circuit, sending, by the computation circuit, the first operation instruction and the second operation instruction to the operation circuit, controlling, by the computation circuit, the operation circuit to execute the first operation instruction to perform feature extraction on the target image to obtain a feature image, and controlling, by the computation circuit, the operation circuit to execute the second operation instruction to perform object classification on the feature image to obtain the target classification result, wherein the first operation instruction is a preset instruction for feature extraction, and the second operation instruction is a preset instruction for object classification.
 3. The method of claim 2, wherein the controlling, by the computation circuit, the operation circuit to execute the second operation instruction to perform object classification on the feature image to obtain the target classification result includes: controlling, by the computation circuit, the operation circuit to execute the second computation instruction to perform object classification on the feature image to obtain a plurality of classification results, wherein the classification results include classification scores, and controlling, by the computation circuit, the operation circuit to select one of the plurality of classification results corresponding to a classification score exceeding a preset threshold as the target classification result.
 4. The method of claim 2, wherein the controlling, by the computation circuit, the operation circuit to execute the first operation instruction to perform feature extraction on the target image to obtain a feature image includes: controlling, by the computation circuit, the operation circuit to perform feature extraction on the target image based on an operation instruction set of at least one thread to obtain a feature image, wherein the operation instruction set includes at least one first operation instruction, and an order of executing the first operation instruction in the operation instruction set is customized by a user side or a terminal side.
 5. The method of claim 1, wherein the controlling, by the computation circuit, the communication circuit to obtain a target image to be processed includes: controlling, by the computation circuit, the communication circuit to obtain an original image to be processed input by a user, and controlling, by the computation circuit, the operation circuit to pre-process the original image to obtain a target image to be processed, wherein the pre-processing includes one or more of the following processing: translation, scaling transformation, non-linear transformation, normalization, format conversion, data deduplication, processing of data exception, and data missing filling.
 6. The method of claim 1, wherein the computation circuit further includes a data access circuit and a storage medium, and the method includes: controlling, by the computation circuit, the operation circuit to send the target classification result to the data access circuit and store the same in the storage medium.
 7. The method of claim 1, wherein the operation circuit includes a primary operation module and a plurality of secondary operation modules, wherein the primary operation module is interconnected with the plurality of secondary operation modules by an interconnection module, and when the operation instruction is a convolution operation instruction, the executing the operation instruction to classify the feature identification objects in the target image includes: controlling, by the computation circuit, the secondary operation modules to implement a convolution operation of input data and a convolution kernel in a convolution neural network algorithm, wherein the input data is the target image and the convolution neural network algorithm corresponds to the convolution operation instruction, controlling, by the computation circuit, the interconnection module to implement data transfer between the primary operation module and the secondary operation modules, before a forward operation of a neural network fully connected layer starts, transferring, by the primary operation module, the input data to each secondary operation module through the interconnection module, and after the computation of the secondary operation modules is completed, splicing, by the interconnection module, output scalars of the respective secondary operation modules stage by stage to obtain an intermediate vector, and sending the intermediate vector back to the primary operation module, and controlling, by the computation circuit, the primary operation module to control intermediate vectors corresponding to all input data into an intermediate result, and performing subsequent operations on the intermediate result, wherein the performing subsequent operations on the intermediate result includes: controlling, by the computation circuit, the primary operation module to add bias data to the intermediate result, and then performing an activation operation.
 8. (canceled)
 9. The method of claim 7,wherein the primary operation module includes a first circuit, wherein the first operation circuit it includes a vector addition circuit and an activation circuit, the controlling, by the computation circuit, the primary operation module to add bias data to the intermediate result, and then performing an activation operation includes: controlling, by the computation circuit, the vector addition circuit to implement a bias addition operation of a convolutional neural network operation and perform element-wise addition on bias data and the intermediate result to obtain a bias result, and controlling, by the computation circuit, the activation circuit to perform an activation function operation on the bias result.
 10. (canceled)
 11. The method of claim 7, wherein each secondary operation module includes a second operation circuit, wherein the second operation circuit includes a vector multiplication circuit and an accumulation circuit, the controlling, by the computation circuit, the secondary operation modules to perform a convolution operation of input data and a convolution kernel in a convolution neural network algorithm includes: controlling, by the computation circuit, the vector multiplication circuit to perform a vector multiplication operation of the convolution operation, and controlling, by the computation circuit, the accumulation t to perform an accumulation operation of the convolution operation.
 12. (canceled)
 13. (canceled)
 14. The method of claim 7, wherein the computation circuit controls the plurality of secondary operation modules to compute respective output scalars in parallel by using the same input data and respective convolution kernels.
 15. A computation circuit, comprising a communication circuit and an operation circuit, wherein the communication circuit is configured to obtain a target image to be processed, the target image comprises feature identification objects which are used to identify the target image or identify objects included in the target image; the operation circuit is configured to obtain an operation instruction to execute the operation instruction to classify feature identification objects in the target image, so as to obtain a target classification result, wherein the target classification result is used to indicate target classifications to which the feature identification objects belong, and the operation instruction is a pre-stored instruction for object classification.
 16. The computation circuit of claim 15, further comprising a register circuit and a controller circuit, wherein the controller circuit is configured to fetch a first operation instruction and a second operation instruction from the register circuit, and send the first operation instruction and the second operation instruction to the operation circuit, the operation circuit is configured to execute the first operation instruction to perform feature extraction on the target image to obtain a feature image, and the operation circuit is configured to execute the second operation instruction to perform object classification on the feature image to obtain a target classification result, wherein the first operation instruction is a preset instruction for feature extraction, and the second operation instruction is a preset instruction for object classification.
 17. The computation circuit of claim 16, wherein the operation circuit is configured to perform feature extraction on the target image based on an operation instruction set of at least one thread to obtain a feature image, wherein the operation instruction set includes at least one first operation instruction, and an order of executing the first operation instruction in the operation instruction set is customized by a user side or a terminal side, and/or the operation circuit is configured to execute the second operation instruction to perform object classification on the feature image to obtain a plurality of classification results, wherein the classification results include classification scores, and the operation circuit is further configured to select a classification result corresponding to a classification score exceeding a preset threshold from the plurality of classification results as the target classification result.
 18. The computation circuit of claim 15, wherein the communication circuit is configured to obtain an original image to be processed input by a user, the operation circuit is configured to pre-process the original image to obtain a target image to be processed, wherein the pre-processing includes one or more of the following processing: translation, scaling transformation, non-linear transformation, normalization, format conversion, data deduplication, processing of data exception, and data missing filling.
 19. The computation circuit of claim 15, wherein the operation circuit includes a primary operation module and a plurality of secondary operation modules, wherein the primary operation module is interconnected with the plurality of secondary operation modules by an interconnection module, and when the operation instruction is a convolution operation instruction, the secondary operation modules are configured to implement a convolution operation of input data and a convolution kernel in a convolution neural network algorithm, wherein the input data is the target image and the convolution neural network algorithm corresponds to the convolution operation instruction, the interconnection module is configured to implement data transfer between the primary operation module and the secondary operation modules, before a forward operation of a neural network fully connected layer starts, the primary operation module sends the input data to each secondary operation module through the interconnection module, and after the computation of the secondary operation modules is completed, the interconnection module splices output scalars of the respective secondary operation modules stage by stage into an intermediate vector and sends the intermediate vector back to the primary operation module, and the primary operation module is configured to splice intermediate vectors corresponding to all input data into an intermediate result, and perform subsequent operations on the intermediate result, wherein the primary operation module is configured to add bias data to the intermediate result, and then perform an activation operation.
 20. (canceled)
 21. (canceled)
 22. (canceled)
 23. The computation circuit of claim 19, wherein the plurality of secondary modules use the same input data and respective convolution kernels to compute respective output scalars in parallel.
 24. (canceled)
 25. (canceled)
 26. (canceled) 